Knowledge bases are collections of facts and data organized in into systematic arrangement of information. Online knowledge bases have become increasingly prevalent on the Internet, and examples include WordNet, Wikipedia, Webopedia, and similar online encyclopedias, dictionaries, and document collections. These knowledge bases are typically organized around individual documents (“articles”) that describe topics of interest, such as persons, places, events, fields of knowledge, and the like. Each article on a particular subject or topic is the primary unit of storage and manipulation. That is, articles as a whole are used to describe a topic, and articles themselves are stored as single document typically containing a large block of unstructured (other than for formatting) text.
More particularly, in a typical online knowledge base such as Wikipedia or Wordnet, search tools are provided to search the knowledge for information. An underlying service engine or database management system receives a search query containing one or more keywords or phrases. The service engine then selects one or more articles that contains such keywords. Typically, the single article that best matches the keyword query, for example, the article having the query keywords in its title, will be retrieved and displayed to the user.
Returning an entire article however does not provide the user with information that quickly indicates which facts or details about the topic were most relevant to the user's query. When the entire article is returns to the user in the search results, the burden is upon the user to analyze the article to determine which portions are relevant to the query. At best, where the search query terms appearing in the title of the article, the user only knows that there is some article that discusses the topic named by the user's query. But this provides no information about which specific facts or details in this article are most important or relevant to an understanding of the topic. The user is left to reading or skimming the entire article to determine which facts may be most interesting or relevant to their purposes. Where the article is provided on the basis of a keyword match outside of the article's title, then perhaps a snippet of text in the body of the article is shown that contains the query keywords. However, the snippet does not necessarily reflect the details or facts about the topic that are most relevant to the user's query. Both of these problems result in part from the storage and retrieval of articles as unstructured content that cannot be dynamically configured based on a user's query.